In many subspecialties of pathology, the intrinsic complexity of
rendering accurate diagnostic decisions is compounded by a lack of
definitive criteria for detecting and characterizing diseases and
their
corresponding histological features. In some cases, there exists a
striking disparity between the diagnoses rendered by recognized
authorities and those provided by non-experts. We previously reported
the development of an Image Guided Decision Support (IGDS) system,
which was shown to reliably discriminate among malignant lymphomas and
leukemia that are sometimes confused with one another during routine
microscopic evaluation. As an extension of those efforts, we report
here a web-based intelligent archiving subsystem that can automatically
detect, image, and index new cells into distributed ground-truth
databases. Systematic experiments showed that through the use of
robust texture descriptors and density estimation based fusion the
reliability
and performance of the governing classifications of the system were
improved significantly while simultaneously reducing the
dimensionality of the feature space.